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Neural networks fundamentals in mobile robot control systems
3. LECTURE: MULTILAYERED FEEDFORWARD STATIC NEURAL NETWORKS
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Table of contents
1. LECTURE: INTRODUCTION TO NEURAL NETWORKS
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2. LECTURE: BASES OF LEARNING OF NEURAL NETWORKS
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3. LECTURE: MULTILAYERED FEEDFORWARD STATIC NEURAL NETWORKS
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3.1. Two layered neural network mathematical description
3.2. Generalized delta rule
3.3. Network with linear output neurons
3.4. Structure of a multi-layered feedforward neural network
3.5. Description of a multi-layered feedforward neural network
3.6. Generalized Delta Rule for MFNN
3.7. Recursive computation of delta
3.8. Momentum BP algorithm
3.9. A Summary of BP learning algorithm
3.10. Some issues in BP learning algorithm
3.11. Local minimum problem
3.12. Problems
Practical training 5
3.13. Task for practical training 5
3.14. Example of the practical training 5 performing
3.15. Variants
3.16. Requirements to the results representation
Practical training 6
3.17 Task for practical training 6
3.18. Example of the practical training 6 performing
3.19. Variants
3.20. Requirements to the results representation
4. LECTURE: ADVANCED METHODS FOR LEARNING NEURAL NETWORKS
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BIBLIOGRAPHY
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